Stabilized Neural Prediction of Potential Outcomes in Continuous Time

2024-10-04

arXiv.org

AI Summary

Existing neural causal methods assume discrete, regularly spaced measurement and intervention timestamps in electronic health records (EHRs), contradicting the irregular sampling and asynchronous interventions characteristic of real-world clinical practice. To address this, we propose SCIP-Net—the first continuous-time neural causal model for EHRs. SCIP-Net introduces the first continuous-time Stable Inverse Propensity Weighting (SIPW) mechanism, enabling dynamic adjustment to time-varying confounders. It jointly integrates neural ordinary differential equations (ODEs) to model latent patient trajectories, continuous-time inverse propensity score estimation, and stabilized weight learning—thereby enabling personalized and robust estimation of conditional average potential outcomes (CAPOs) under interventions at arbitrary time points. Experiments on real-world EHR data demonstrate that SCIP-Net significantly improves accuracy, generalizability, and out-of-distribution temporal extrapolation of CAPO estimates, overcoming fundamental limitations of discrete-time modeling paradigms.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Patient trajectories from electronic health records are widely used to estimate conditional average potential outcomes (CAPOs) of treatments over time, which then allows to personalize care. Yet, existing neural methods for this purpose have a key limitation: while some adjust for time-varying confounding, these methods assume that the time series are recorded in discrete time. In other words, they are constrained to settings where measurements and treatments are conducted at fixed time steps, even though this is unrealistic in medical practice. In this work, we aim to estimate CAPOs in continuous time. The latter is of direct practical relevance because it allows for modeling patient trajectories where measurements and treatments take place at arbitrary, irregular timestamps. We thus propose a new method called stabilized continuous time inverse propensity network (SCIP-Net). For this, we further derive stabilized inverse propensity weights for robust estimation of the CAPOs. To the best of our knowledge, our SCIP-Net is the first neural method that performs proper adjustments for time-varying confounding in continuous time.

Problem

• Estimate CAPOs in continuous time
• Adjust for time-varying confounding
• Model irregular treatment timestamps

Innovation

• Continuous time modeling
• Stabilized inverse propensity weights
• Time-varying confounding adjustment